NIA Pilot Research Grant Program. Priority Area 24: Improved Measures and Methodologies. Residential mobility in retirement is partly driven by health. Positive health status promotes mobility early in retirement, and negative health status later in retirement. The central concern of this project is to discover how well expectations of a move predict an actual move for late pro-retirement and early post-retirement movers. A behavioral model of migration decision-making guides our effort. Our expectations, with regard to our specific aims, are: Specific Aim I: To determine the nature of the relationship between 1) resource variables, including and monetary resources, 2) travel experience, and 3) community ties at origin, and mobility, both expected and actual. Specific Aim 2: To determine the nature of the relationship between travel experience and the likelihood of mobility, both expected and actual. Specific Aim 3: To determine the nature of the relationship between community ties and the likelihood of mobility, both expected or actual. Specific Aim 4: To determine the nature of the relationship between expected mobility and actual mobility. Specific Aim 5: To determine whether the net effect of variables predicting mobility depend upon gender, labor force status, or race/ethnic background. The study will use data from the Health and Retirement Study (HRS). The 1992 wave of the data contains measures of resources, travel experience, community ties, and expected mobility. A measure of actual mobility between 1992 and 1994 is available from the second wave of the HRS. The core of the proposed analysis will employ multinomial and binomial logistic regression in order to model expected mobility and actual mobility respectively. Additionally the proposed analytical design (1) accounts for statistical issues arising from the complex sampling design used by the HRS by making use of SUDAAN software and (2) seeks to explore couple-level dynamics that may shape mobility behavior and attitudes. Specific expectations are articulated with respect to the relationships between and among key predictor variables and a modeling strategy to evaluate these expectations is delineated.